Yuki Juan’s Systems Biology Lab Systems Biology Systems Biology Hsueh-Fen Juan ( Hsueh-Fen Juan ( 阮阮阮 阮阮阮 ) ) NTUT NTUT Aug 29, 2003 Aug 29, 2003
Yuki Juan’s Systems Biology Lab
Systems BiologySystems Biology
Hsueh-Fen Juan (Hsueh-Fen Juan (阮雪芬阮雪芬 ))NTUTNTUTAug 29, 2003Aug 29, 2003
juan SBL
OutlineOutline
IntroductionIntroduction To understand biological systemsTo understand biological systems Protein—protein interactionProtein—protein interaction Drug DiscoveryDrug Discovery Case study: effect of RGD-peptides in Case study: effect of RGD-peptides in
breast cancerbreast cancer
juan SBL
OutlineOutline
IntroductionIntroduction To understand biological systemsTo understand biological systems Protein—protein interactionProtein—protein interaction Drug DiscoveryDrug Discovery Case study: effect of RGD-peptides in Case study: effect of RGD-peptides in
breast cancerbreast cancer
juan SBL
Traditional Biology & Traditional Biology & Systems BiologySystems Biology Traditional biologyTraditional biology : :
– Single genes or proteinsSingle genes or proteins
Systems biologySystems biology: : – Simultaneously study the complex Simultaneously study the complex
interaction of many levels of biological interaction of many levels of biological information to understand how they work information to understand how they work togethertogether
Genomic DNAGenomic DNA mRNA mRNA ProteinsProteins
Functional proteinsFunctional proteinsInformational pathways Informational pathways Informational networksInformational networks
juan SBL
Systems Biology and OmicSystems Biology and Omics Datas Data
Systems BiologySystems Biology
GeneticGenetic
ProteomicProteomic
TranscriptomicTranscriptomic
MetabonomicMetabonomic
Drug discovery Drug discovery Development processDevelopment process
Understanding drug toxicologyUnderstanding drug toxicology
juan SBL
The Aims of Systems The Aims of Systems BiologyBiology What are the basic structures and
properties of a biological network? How does a biological system behave
over time under various conditions? How does a biological system
maintain its robustness and stability? How can we modify or construct
biological systems to achieve desired properties?
juan SBL
To Understand To Understand Biological SystemsBiological Systems System structure identificationSystem structure identification System behavior analysisSystem behavior analysis System controlSystem control System designSystem design
juan SBL
OutlineOutline
IntroductionIntroduction To understand biological systemsTo understand biological systems Protein—protein interactionProtein—protein interaction Drug DiscoveryDrug Discovery Case biology study: effect of RGD-peCase biology study: effect of RGD-pe
ptides in breast cancerptides in breast cancer
juan SBL
System Structure System Structure IdentificationIdentification Network structure identificationNetwork structure identification
– KEGG and EcoCycKEGG and EcoCyc Parameter identificationParameter identification
– Genetic algorithmsGenetic algorithms
juan SBL
KEGGKEGG
http://www.genome.ad.jp/kegg/
juan SBL
Pathway in KEGGPathway in KEGG
juan SBL
EcoCycEcoCyc
http://www.ecocyc.org/http://www.ecocyc.org/
juan SBL
Pathway in EcoCycPathway in EcoCyc
juan SBL
Genetic AlgorithmsGenetic Algorithms
Based on the underlying genetic process
They are replicated and passed onto the next generation with selection depending on fitness.
Genetic information can be changed through genetic operations.
juan SBL
Three Main Operations Three Main Operations in GAin GA Selection Crossover Mutation
juan SBL
Genetic AlgorithmsGenetic Algorithms
juan SBL
System Behavior System Behavior AnalysisAnalysis SimulationSimulation Analysis methodsAnalysis methods
juan SBL
Software Tools for Software Tools for Systems Biology and Systems Biology and Their WorkflowTheir Workflow Relationship among software Relationship among software
toolstools Workflow and software toolsWorkflow and software tools
juan SBL
Relationship among Relationship among Software ToolsSoftware Tools
Experimental data Experimental data databasedatabase
Experimental data Experimental data interfaceinterface
Measurement Measurement systemssystems
Genome/proteomeGenome/proteomedatabasedatabase
System structure System structure databasedatabase
SimulatorSimulator
System analysisSystem analysismodulemodule
System profileSystem profiledatabasedatabase
Visualization moduleVisualization module
Parameter optimiztionParameter optimiztionmodulemodule
Hypotheses generation Hypotheses generation experiment planning experiment planning
modulemodule
juan SBL
Workflow and Workflow and Software ToolsSoftware Tools
Expression profile dataTwo-hybrid dataRT-PCR data, etc.
Gene regulation networkMetabolic cascade networkSignal transduction network
Parameter optimizer
Simulator
Hypothesisgenerator
Biological experiments
A set of plausible hypothesisPredictions of gene and interactions
Experiment designAssistance system
Experiment plans
Dynamic system analysisRobustness stability, bifurcation, etcDesign pattern analysisDesign patter extraction
juan SBL
Robustness of Robustness of Biological SystemBiological System System controlSystem control RedundancyRedundancy Modular designModular design Structural stabilityStructural stability
juan SBL
System ControlSystem Control
Feedforward ControlFeedforward Control Feedback ControlFeedback Control
juan SBL
Feedforward Control and Feedforward Control and Feedback ControlFeedback Control
ControlleControllerr
EffectorEffectorinputinput outputoutput
Feedforward controlFeedforward control
ControlleControllerr
EffectorEffectorinputinput outputoutput
Feedback controlFeedback control
juan SBL
Heat Shock Response with FeHeat Shock Response with Feedforward and Feedback Conedforward and Feedback Controltrol
Heat ShockHeat Shock
Misfolded Misfolded ProteinProtein
Normal Normal ProteinProtein rpoHrpoH
EE
7070
3232
3232
3232dnaKdnaKdnaJdnaJgrpEgrpE
dnaKdnaKdnaJdnaJgrpEgrpEGroESGroESGroELGroEL
hsphspdnaKdnaKdnaJdnaJGroESGroESGroELGroEL
juan SBL
Redundancy in MAP kinase Redundancy in MAP kinase cascadecascade
MEKK1, MLK3Raf, Mos
MEK1,2/MKK1,2
MAPK/ERK
SEK1, 2/MKK4,7
SAPK/JNK
ASK1, TAK1
MKK3,6
p38
TranscriptionTranscription
juan SBL
Modular DesignModular Design
Component: Component: – An elemnetary unit of the systemAn elemnetary unit of the system– Genes and proteinsGenes and proteins
Device:Device:– An minimum unit of the functional assemblyAn minimum unit of the functional assembly– Transcription complexes and replication complexesTranscription complexes and replication complexes
Module:Module:– A large cluster of devicesA large cluster of devices– Organells and gene regulatory circuits for the cell cycleOrganells and gene regulatory circuits for the cell cycle
SystemSystem– A top-level assembly of modulesA top-level assembly of modules– A cell or entire animalA cell or entire animal
juan SBL
Structural StabilityStructural Stability
Play important roles in developmentPlay important roles in development Temporal arrangement of signaling iTemporal arrangement of signaling i
nn– the JAK/STAS signaling pathwaythe JAK/STAS signaling pathway– pattern formation in pattern formation in DrosophilaDrosophila involvi involvi
ng Ubx and Dpp ng Ubx and Dpp
juan SBL
Genome, Proteome, and SGenome, Proteome, and Systeomeysteome
Dynamics informationDynamics information
High resolutionimage
Expression profileProtein interactions, etc.
Proteome
Genome
Components Components informationinformation
Basic model informationBasic model information
Parameters
Gene network modelMetabolic pathway modelSignal transduction model
Basic structure
Individual genetic variationsIndividual genetic variations
System dynamicsanalysis
Individual sequence variation
Individual heterochromatinvariation
Mutation analysis
Drug sensitivityanalysis
System System dynamics dynamics informationinformation
Individual Individual SysteomeSysteome
juan SBL
OutlineOutline
IntroductionIntroduction To understand biological systemsTo understand biological systems Protein—protein interactionProtein—protein interaction Drug DiscoveryDrug Discovery The systems biology study: sffect of The systems biology study: sffect of
RGD-peptides in breast cancerRGD-peptides in breast cancer
juan SBL
Introduction to Protein—pIntroduction to Protein—protein Interactionrotein Interaction Protein-protein interactions are Protein-protein interactions are
intrinsic to every cellular process.intrinsic to every cellular process. Form the basis of phenomenaForm the basis of phenomena
-DNA replication and transcription-DNA replication and transcription
-Metabolism -Metabolism
-Signal transduction-Signal transduction
-Cell cycle control -Cell cycle control
-Secretion-Secretion
juan SBL
PPIPPI
Knowledge of interacting proteins
Provide insight into the function of important genes
Elucidates relevant pathways
Facilitates the identification of potential drug targets
Use in developing novel therapeutics
juan SBL
The Study of Protein-The Study of Protein-protein Interaction by protein Interaction by Mass SpectrometryMass Spectrometry
bait
S14
??
??
* *
**
SDS-PAGE
MASS
juan SBL
Peptide Mass Peptide Mass FingerprintingFingerprinting
juan SBL
Yeast Two-hybrid Yeast Two-hybrid SystemSystem
Useful in the study of various interactionsUseful in the study of various interactions The technology was originally developed The technology was originally developed
during the late during the late 1980's1980's in the laboratory Dr. in the laboratory Dr. Stanley Fields (see Fields and Song, 1989, Stanley Fields (see Fields and Song, 1989, NatureNature). ).
juan SBL
Yeast Two-hybrid Yeast Two-hybrid AssayAssay
GAL4 DNA-binding
domain
GAL4 DNA-activation domain
Nature, 2000
juan SBL
Yeast Two-hybrid Yeast Two-hybrid AssayAssay Library-based Library-based
yeast two-yeast two-hybrid hybrid screening screening methodmethod
Nature, 2000
juan SBL
Protein-protein Protein-protein Interactions on the Interactions on the WebWeb
Yeast Yeast http://depts.washington.edu/sfields/yplm/data/indexhttp://depts.washington.edu/sfields/yplm/data/index.htm.htmll http://portal.curagen.comhttp://portal.curagen.com http://mips.gsf.de/proj/yeast/CYGD/interaction/http://mips.gsf.de/proj/yeast/CYGD/interaction/ http://www.pnas.org/cgi/content/full/97/3/1143/DC1http://www.pnas.org/cgi/content/full/97/3/1143/DC1 http://dip.doe-mbi.ucla.edu/http://dip.doe-mbi.ucla.edu/ http://genome.c.kanazawa-u.ac.jp/Y2Hhttp://genome.c.kanazawa-u.ac.jp/Y2H C. ElegansC. Elegans http://cancerbiology.dfci.harvard.edu/cancerbiology/ResLabs/Vidal/http://cancerbiology.dfci.harvard.edu/cancerbiology/ResLabs/Vidal/ H. PyloriH. Pylori http://pim/hybrigenics.comhttp://pim/hybrigenics.com DrosophilaDrosophila http://gifts.univ-mrs.fr/FlyNets/Flynets_home_page.htmlhttp://gifts.univ-mrs.fr/FlyNets/Flynets_home_page.html
juan SBL
Yeast Protein Linkage Yeast Protein Linkage Map DataMap Data
New protein-protein interactions in yeastNew protein-protein interactions in yeast
Stanley Fields Lab
http://depts.washington.edu/sfields/yplm/data
juan SBL
GeneScapeGeneScape
PathwayCalling: Protein interaction PathwayCalling: Protein interaction and pathway Analysisand pathway Analysis
http://portal.curagen.com
juan SBL
Munich Information Munich Information Center for Protein Center for Protein SequencesSequences
MIPS: a database for genomes and protein MIPS: a database for genomes and protein sequencessequences
The MIPS Comprehensive Yeast Genome The MIPS Comprehensive Yeast Genome Database (CYGD) aims to present information Database (CYGD) aims to present information on the molecular structure and functional on the molecular structure and functional network.network.
http://mips.gsf.de/
juan SBL
Yeast Interacting Yeast Interacting Proteins DatabaseProteins Database
http://genome.c.kanazawa-u.ac.jp/Y2H
juan SBL
SuisekiSuiseki
•DNA replication •The Immune System •The E2F transcription factor •The talin/viniculin/actin system
is a system for the extraction of protein-protein interactions from large collections of scientific text
http://www.pdg.cnb.uam.es/suiseki/
juan SBL
Suiseki Suiseki
juan SBL
SuisekiSuiseki
RegulateRegulate ActivateActivate
juan SBL
Information Extraction Information Extraction (IE)(IE) A vast amount of data on protein-A vast amount of data on protein-
protein interactions residues in protein interactions residues in the published literature, which the published literature, which never been entered into never been entered into databases.databases.
IE have been applied to gaining IE have been applied to gaining information on protein-protein information on protein-protein interactions.interactions.
juan SBL
Mining Literature for Mining Literature for Protein-protein Protein-protein InteractionsInteractions
juan SBL
Extraction of the Extraction of the InteractionsInteractions The nouns and verbs are taken from The nouns and verbs are taken from
a hand constructed list containing na hand constructed list containing nouns such as ouns such as activationactivation, , phosphorylphosphorylationation or or interactioninteraction, and verbs such , and verbs such as as activatesactivates, , bindsbinds oror phosphorylatephosphorylatess. Rules are applied directly to the te. Rules are applied directly to the text by string comparison.xt by string comparison.
Comp Funct Genom 2001, 2, 196-206
juan SBL
Extraction of the Extraction of the InteractionsInteractions The sentence “The expressed The sentence “The expressed p53p53 p p
rotein showed nuclear localization arotein showed nuclear localization and its expression was associated witnd its expression was associated with an induction of h an induction of p21p21 and and baxbax expres expression” relates sion” relates p53p53 with with p21p21 and and baxbax but does not imply a physical interacbut does not imply a physical interaction between them.tion between them.
Comp Funct Genom 2001, 2, 196-206
juan SBL
OutlineOutline
IntroductionIntroduction To understand biological systemsTo understand biological systems Protein—protein interactionProtein—protein interaction Drug DiscoveryDrug Discovery The systems biology study: sffect of The systems biology study: sffect of
RGD-peptides in breast cancerRGD-peptides in breast cancer
juan SBL
Linkage of a Basic System-Linkage of a Basic System-Biology Research Cycle with Biology Research Cycle with Drug Discovery and Drug Discovery and Treatment CyclesTreatment Cycles
Nature 2002, 420, 206..
juan SBL
Mammalian System-microbiMammalian System-microbial-nutritional-xenobiotic Intal-nutritional-xenobiotic Interactionseractions
Nature 2003, 2, 668.
juan SBL
Possible InteractionsPossible Interactions
Nature 2003, 2, 668.
juan SBL
The Dynamic Pachinko The Dynamic Pachinko Model of MetabolismModel of Metabolism
Nature 2003, 2, 668.
juan SBL
OutlineOutline
IntroductionIntroduction To understand biological systemsTo understand biological systems Protein—protein interactionProtein—protein interaction Drug DiscoveryDrug Discovery Case study: effect of RGD-peptides Case study: effect of RGD-peptides
in breast cancerin breast cancer
juan SBL
Effect of RGD-peptides Effect of RGD-peptides in breast cancerin breast cancer
IntroductionIntroduction cDNA microarray cDNA microarray ProteomicsProteomics BioinformaticsBioinformatics
Yuki Juan’s Systems Biology Lab
IntroductionIntroduction
juan SBLSCIENCE, 2001, 294, 82-85
juan SBL
The Structure of an IntegriThe Structure of an Integrinn HynesHynes in 1987 to emin 1987 to em
phasize the role of thphasize the role of these RGD receptors in ese RGD receptors in integratingintegrating the extra the extracellular matrix outsicellular matrix outside the cell with the ade the cell with the actin-containing cytosctin-containing cytoskeleton inside the cekeleton inside the cell.ll.
juan SBL
The Interactions of Integrins The Interactions of Integrins with Other Proteins on both Siwith Other Proteins on both Sides of the Lipid Bilayerdes of the Lipid Bilayer
juan SBL
Schematic Model of the ProteiSchematic Model of the Protein-protien Interactions of a Focan-protien Interactions of a Focal Adhesion Complexl Adhesion Complex
Signal are presumably Signal are presumably transmitted into the transmitted into the nucleus, where they nucleus, where they stimulate the stimulate the transcription of gene transcription of gene involved in involved in cell growthcell growth and and proliferationproliferation
juan SBL
Integrin-activated Survival SiIntegrin-activated Survival Signalsgnals
Integrins
Shc
FAK
Grb2/Sos
PI 3-kinase
Ras Raf MEK MAPK
Cell survival
Trends in Cell Biology, 1997, 7, 146-150
juan SBL
How RGD Trigger How RGD Trigger Apoptosis?Apoptosis?
By integrin-mediated signal? By integrin-mediated signal? Directly interact with the protein in cDirectly interact with the protein in c
ytosol?ytosol?
juan SBL
How RGD Trigger How RGD Trigger Apoptosis?Apoptosis?
b. RGD trigger apoptosis via integrin
Nature, 1999, 397, 534-539
a. Cell survive
c. Cell apoptosis by activating procaspase-3
juan SBL
Control Aggregation Cell Death
RGD(Arg-Gly-Asp) is the smallest motif that bind with the integrin receptor on the cell surface and play important role in cell cycle.
RGD and Cell DeathRGD and Cell Death
Yuki Juan’s Systems Biology Lab
Our StudyOur Study
juan SBL
Human breast cancer cell MCF-7
Cell Apoptosis
Genomic Study
Proteomics
Bioinformatics
Our Study
juan SBL
The Structures of RGD The Structures of RGD Mimetic PeptidesMimetic Peptides
Asp
GlyArg
NH
H2N O
O
N
O
HN
NH
O
O
OH
HN
O
HN
O
S
S
HN
O
NH
NH
H2N
ArgGly Asp
Trp
Pro
Cys
Tpa
Cyclic-RGD
juan SBL
RGD cRGDcontrol
1mM
5mM
0.5mM
1mM
control
Yuki Juan’s Systems Biology Lab
cDNA MicroarraycDNA Microarray
juan SBL
Introduction to MicroarrayIntroduction to Microarray After the draft of Human genome project was puAfter the draft of Human genome project was pu
blished and the powerful high–throughput microblished and the powerful high–throughput microarray technology is available, the discovery of disarray technology is available, the discovery of discriminating gene patterns becomes important.criminating gene patterns becomes important.
cDNA microarray technology is a powerful approcDNA microarray technology is a powerful approach to accurately measure changes in global mRach to accurately measure changes in global mRNA expression levels.NA expression levels.
This technique has been used to discover novel gThis technique has been used to discover novel genes, determine gene functions, evaluate drugs, enes, determine gene functions, evaluate drugs, dissect pathways, and classify clinical samples.dissect pathways, and classify clinical samples.
juan SBL
A Framework of Microarray Analysis
Experiments Designing
Microarrary Analysis
Image Analysis
Data Analysis
Data Preprocessing(Normalization & Data Filtering )
juan SBL
cDNA MicroarraycDNA Microarray
C-RGD, 6hr C-RGD, 24hr
C-RGD, 48hr C-RGD, 72hr
juan SBL
Apoptosis Apoptosis
Total Total 3434 genes, but after genes, but after filtering there are only filtering there are only 1919 genesgenes
Total Total 1111 genes have genes have expression fold >2 (up or expression fold >2 (up or down changes)down changes)
juan SBL
Apoptosis RegulatorApoptosis Regulator
U60519
U97075
AF051941
U13738
AF005775
U60521
Z48810
AAF19819
U67319
U28976
AF015450
juan SBL
DescriptionGenebankaccession
No.
6 hFold Change
24 hFold Change
48 hFold Change
72 hFold Change
Group 1
caspase 10, apoptosis-related cysteine protease U60519 - - - 0.471
CASP8 and FADD-like apoptosis regulator U97075 - - - 0.355
nucleoside diphosphate kinase type 6 (inhibitorof p53-induced apoptosis-alpha) AF051941 - - - 0.376
Group 2
caspase 3, apoptosis-related cysteine protease U13738 - 2.301 - -
CASP8 and FADD-like apoptosis regulator AF005775 - 2.272 - -
Group 3
caspase 9, apoptosis-related cysteine protease U60521 - - 2.519 -
Group 4
caspase 4, apoptosis-related cysteine protease Z48810 2.615 - 2.796 2.819
Group 5
inhibitor of apoptosis protein AAF19819 - - - 5.249
caspase 7, apoptosis-related cysteine protease U67319 - - - 2.19
caspase 4, apoptosis-related cysteine protease U28976 - - - 2.603
Group 6
CASP8 and FADD-like apoptosis regulator AF015450 - - - 6.912
Apoptosis RegulatorApoptosis Regulator
juan SBL
6 7224 48
time (hour)0.01
0.1
1
10
Normalized Intensity(log scale)
p1
6 7224 48
time (hour)0.01
0.1
1
10
Normalized Intensity(log scale)
p1
6 7224 48
time (hour)0.01
0.1
1
10
Normalized Intensity(log scale)
p1
6 7224 48
time (hour)0.01
0.1
1
10
Normalized Intensity(log scale)
p1
6 7224 48
time (hour)0.01
0.1
1
10
Normalized Intensity(log scale)
p1
6 7224 48
time (hour)0.01
0.1
1
10
Normalized Intensity(log scale)
p1
juan SBL
Using Linear Model to Using Linear Model to Construct Gene NetworkConstruct Gene Network
Linear ModelLinear Model
ti;,btywΔt
tΔy
jiji,j
i
tBtyWt
ty
,
1~~~
YYYt
YW TT
D’haeseleer, 2000
juan SBL
Weights Matrix of Weights Matrix of Apoptosis RegulatorApoptosis Regulator
Weights Gene 1 Gene 2
2.670363 AF015450 U60521
2.068236 AAF19819 U60521
-1.889373 AF015450 AAF19819
-1.427408 AAF19819 AAF19819
-0.81632 AF005775 AF005775
-0.761848 U13738 AF005775
0.753277 AF015450 U60519
0.682257 U13738 AAF19819
0.646907 Z48810 AF005775
0.636552 AF015450 AF005775
0.632796 AF005775 Z48810
0.594627 AF005775 AAF19819
-0.55848 Z48810 AAF19819
0.543142 AAF19819 U60519
-0.527872 U60521 U60521
0.518056 U28976 U60521
0.508007 U60521 AF005775
0.499483 U13738 Z48810
juan SBL
Gene Network of Gene Network of Apoptosis RegulatorApoptosis Regulator caspase 9, apoptosis-related
cysteine protease (U60521)
inhibitor of apoptosis protein (AAF19819)
CASP8 and FADD-like apoptosis regulator
(AF015450)
+2.068236
+2.670363
6
-1.889373
caspase 3, apoptosis-related cysteine protease
(U13738)
+0.682257
-0.761848
CASP8 and FADD-like apoptosis regulator
(AF005775)
caspase 10, apoptosis-related cysteine protease
(U60519)
caspase 4, apoptosis-related cysteine protease
(Z48810)
+0.646907
+0.636552
+0.753277
juan SBL
J03071 X15215 S75361 M37483 U14187 D12614 NM_005130 AF179274 AB017365 AF035835 D25328 NM_003242 S81439 AB017364 L13858 L27475 AF068868 M34480 NM_005928 AF005271 X53038 AI127370 AF002986
M37763 AF107885 AF081513 AJ000185 AF251118
D10202
U12535 AF026692
X14253
X51602 AF119815 U72338 AF041240 M37435
AB000509
AI634668 S77035 AF107885 U52112 AF056087
AF019634 X76079
BE336944 X52599 L24494 M64347 M12783 AF107885
U52112 M83575 L13857 AI692949 AW663903 AJ222700 M57399 U31176 X03438 AI885899 AB009249 U73737 U66406 AF266504 AW887370 AB039723 M77227 AF010312 M35878 NM_005429 AAC17439 U28054 L13858 L34641
X14253
NM_004791 AF035835 D63395 U94888 J03071 D87845 L13857 M21188 NM_004114 X14885 X70340
1 2
Signal TransducerSignal Transducer
juan SBL
Weights Gene1 Gene2
-0.875598 NM_003242 L274750.834468 M37435 L27476-0.78655 L27475 L27477
-0.760695 M21188 L27478-0.728793 D63395 L274790.645775 NM_003242 NM_005429
0.608239 NM_003242 NM_005430
0.558504 L27475 NM_005431
0.538674 D63395 NM_005432
0.530764 U72338 L274750.455149 AF041240 L274760.453129 AI634668 L27477-0.452645 AB017364 L274780.449854 NM_003242 U12535
0.444301 M21188 AF251118
-0.442259 M37435 U52112
0.437687 M21188 U12535
0.429097 NM_003242 AF251118
Weights Matrix of Weights Matrix of Signal TransducerSignal Transducer
juan SBL
transforming growth factor,
beta receptor II (70-80kD)
(NM_003242) Human interleukin-1 beta
converting enzyme gene, 5' flank.
(L27475)
colony stimulating
factor 1 (macrophage)
( M37435)
insulin-degrading enzyme
(M21188)
Notch (Drosophila)
homolog 4
(D63395)
-0.875598
+0.834468
vascular endothelial growth factor C
(NM_005429)
+0.645775
+0.608239
+0.558504
Human platelet activating factor
acetylhydrolase, brain isoform, 45
kDa subunit (LIS1) gene, exon 7.
(U72338)
hypocretin (orexin)
neuropeptide precursor
(AF041240)
+0.455149
msh (Drosophila) homeo box
homolog 1 (formerly homeo box 7)
(AI634668)
+0.453129 frizzled (Drosophila) homolog 2
(AB017364)
epidermal growth factor
receptor pathway substrate 8
(U12535)
+0.449854
Interleukin-1 Superfamily z
(AF251118)
+0.444301
+0.530764
-0.452645
-0.728793
+0.538674
-0.760695
Gene Network of Gene Network of Signal TransducerSignal Transducer
Yuki Juan’s Systems Biology Lab
ProteomicsProteomics
juan SBLNature 2000, 405, 837-846
Two-dimensional Gel Approach
juan SBL
Control cRGD
97000
66000
45000
30000
14400
20100
4.0 5.0 6.0 7.0 10.08.0 9.03.2 5.5 4.0 5.0 6.0 7.0 10.08.0 9.03.2 5.5
1
Control vs c-RGD (6hr)Control vs c-RGD (6hr)
juan SBL 4.0 5.0 6.0 7.0 10.08.0 9.03.2 5.5
97000
66000
45000
30000
14400
20100
4.0 5.0 6.0 7.0 10.08.0 9.03.2 5.5
2
34
5 6 78
910
11 13
14
121516
17
18
Control c-RGD
Control vs c-RGD (24hr)Control vs c-RGD (24hr)
juan SBL 4.0 5.0 6.0 7.0 10.08.0 9.03.2 5.5
97000
66000
45000
30000
14400
20100
4.0 5.0 6.0 7.0 10.08.0 9.05.5
19 20
21 22
Control vs c-RGD (48hr)Control vs c-RGD (48hr)
Control c-RGD
juan SBL 4.0 5.0 6.0 7.0 10.08.0 9.03.2 5.5
97000
66000
45000
30000
14400
20100
4.0 5.0 6.0 7.0 10.08.0 9.03.2 5.5
23
24 25
26
Control vs c-RGD (72hr)Control vs c-RGD (72hr)
Control c-RGD
juan SBL
Proteomics ResultsProteomics Results
1. Semenogelin I protein precursor (S1. Semenogelin I protein precursor (SGI)GI)
2. Cell division protein kinase 6 2. Cell division protein kinase 6 3. 3. Zinc finger protein 74 isoform 4Zinc finger protein 74 isoform 4 4. K4. Keratin eratin 5. Similar to presomitic mesoderm sp5. Similar to presomitic mesoderm sp
ecific geneecific gene 6. Unnamed protein product6. Unnamed protein product 7. RNA-binding protein regulatory su7. RNA-binding protein regulatory su
bunit bunit 8. Similar to Claudin-6 (Skullin)8. Similar to Claudin-6 (Skullin) 9. Unnamed protein product 9. Unnamed protein product 10. Similar to Per-hexamer repeat pro10. Similar to Per-hexamer repeat pro
tein 5tein 5 11. Similar to L1 repetitive element O11. Similar to L1 repetitive element O
RFRF 12. Hypothetical protein12. Hypothetical protein 13. 13. Zinc finger protein 189 Zinc finger protein 189
ISOFORM 2ISOFORM 2
14. Hypothetical protein 14. Hypothetical protein 15. Similar to stretch response prote15. Similar to stretch response prote
in 553in 553 16. Zinc finger protein 8316. Zinc finger protein 83 17. Immunoglobulin heavy chain var17. Immunoglobulin heavy chain var
iable regioniable region 18. Hypothetical protein18. Hypothetical protein 19. Cytokeratin 819. Cytokeratin 8 20. Cytokeratin 820. Cytokeratin 8 21. Zinc-alpha-2-glycoprotein precu21. Zinc-alpha-2-glycoprotein precu
rsorrsor 22. Keratin 1822. Keratin 18 23. Platelet-activating factor acetylh23. Platelet-activating factor acetylh
ydrolase precursor ydrolase precursor 24. Topoisomerase II alpha24. Topoisomerase II alpha 25. 13kD differentiation-associated 25. 13kD differentiation-associated
proteinprotein 26. Purified protein derivative-specif26. Purified protein derivative-specif
ic T-cell receptor beta chainic T-cell receptor beta chain Red color: up-regulatedWhite color: down-regulated
Yuki Juan’s Systems Biology Lab
BioinformaticsBioinformatics
juan SBL
RGD Peptides Can Be RGD Peptides Can Be Used in Many DiseasesUsed in Many Diseases
ThrombosisThrombosis OsteoporosisOsteoporosis CancerCancer
Any one elseAny one else
????
juan SBL
Blood Clots FormBlood Clots Form
Blood clots form whBlood clots form when platelets adhere en platelets adhere to one another throto one another through fibrinogen bridgugh fibrinogen bridges that bind to the pes that bind to the platelet integrinlatelet integrin
juan SBL
Clustering Analysis of Clustering Analysis of ProteinsProteins
http://uranus.csie.ntu.edu.tw:9000/index.jsp
juan SBL
RGD-containing RGD-containing Proteins in Swiss-Prot Proteins in Swiss-Prot DatabaseDatabase In Swiss-Prot database, there areIn Swiss-Prot database, there are 738738
human RGD-containing proteins whichuman RGD-containing proteins which containing h containing 5 5 caspase proteins .caspase proteins .– Caspase 1, caspase 2, caspase 3 and caspCaspase 1, caspase 2, caspase 3 and casp
ase7, caspase 8.ase7, caspase 8.
juan SBL
SM22 Leiomyoma
RGD-containing Proteins RGD-containing Proteins in Swiss-Prot Databasein Swiss-Prot Database
Heat shock protein DnaHeat shock protein DnaChaperone DnaJChaperone DnaJ Alzheimer's diseaseAlzheimer's disease
Yuki Juan’s Systems Biology Lab
Conclusion and Conclusion and DiscussionDiscussion
juan SBL
Conclusion and Conclusion and Discussion Discussion
Cyclic RGD exerts more potency than that of liner RGCyclic RGD exerts more potency than that of liner RGD on the inhibiting cell growth.D on the inhibiting cell growth.
The cyclic RGD exerts The cyclic RGD exerts 8-10 times8-10 times potency more than t potency more than that of liner RGD peptide in inhibiting proliferation anhat of liner RGD peptide in inhibiting proliferation and inducing clustering of MCF-7 cells.d inducing clustering of MCF-7 cells.
Cyclic RGD can induce the apoptosis of MCF7. We shoCyclic RGD can induce the apoptosis of MCF7. We showed many caspases involved in this apoptosis and cowed many caspases involved in this apoptosis and constructed the caspase pathway.nstructed the caspase pathway.
juan SBL
Conclusion and Conclusion and DiscussionDiscussion
CASP8 and FADD-like apoptosis regulatorCASP8 and FADD-like apoptosis regulator, , caspase 9caspase 9 and and inhibitor of apoptosis proteininhibitor of apoptosis protein formed the positive and n formed the positive and negative feedback control system.egative feedback control system.
Vascular endothelial growth factor CVascular endothelial growth factor C and and human interlehuman interleukin-1 beta converting enzyme geneukin-1 beta converting enzyme gene have the important have the important positions in the gene network because they will affected positions in the gene network because they will affected many other genes.many other genes.
Clustering tool maybe could predict some novel functioClustering tool maybe could predict some novel functions in RGD-containing proteins.ns in RGD-containing proteins.
juan SBL
OutlookOutlook
cDNA microarray
Drug discovery
Proteomics
Apoptosis pathway
Cellular mechanism
Bioinformatics
juan SBL
SummarySummary
Systems biology is a new and Systems biology is a new and emerging field in biology.emerging field in biology.
Systems biology requires a range Systems biology requires a range of new analysis techniques, of new analysis techniques, measurement technologies, measurement technologies, experimental methods, software experimental methods, software tools.tools.
Systems Biology will be the Systems Biology will be the dominant paradigm in biology.dominant paradigm in biology.
juan SBL
juan SBL
Thank you!Thank you!